{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from urllib import request\n",
    "import json\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data(id):\n",
    "    url = 'http://stock2.finance.sina.com.cn/futures/api/json.php/IndexService.getInnerFuturesMiniKLine60m?symbol='\n",
    "    # url = 'http://stock2.finance.sina.com.cn/futures/api/json.php/IndexService.getInnerFuturesMiniKLine5m?symbol='\n",
    "    url = url + id\n",
    "    req = request.Request(url)\n",
    "    rsp = request.urlopen(req)\n",
    "    res = rsp.read()\n",
    "    res_json = json.loads(res)\n",
    "\n",
    "\n",
    "    bar_list = []\n",
    "\n",
    "    res_json.reverse()\n",
    "    for line in res_json:\n",
    "        bar = {}\n",
    "        bar['datetime'] = line[0]\n",
    "        bar['open'] = float(line[1])\n",
    "        bar['high'] = float(line[2])\n",
    "        bar['low'] = float(line[3])\n",
    "        bar['close'] = float(line[4])\n",
    "        bar['volume'] = int(line[5])\n",
    "        bar_list.append(bar)\n",
    "\n",
    "    df = pd.DataFrame(data=bar_list)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "http://stock2.finance.sina.com.cn/futures/api/json.php/IndexService.getInnerFuturesMiniKLine60m?symbol=RM2201\n"
     ]
    }
   ],
   "source": [
    "dfrm = get_data('RM2201')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot  as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "dfrm[['open','close']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 驱动器 F 中的卷是 学习记录\n",
      " 卷的序列号是 387D-37AA\n",
      "\n",
      " F:\\OSS\\jupyter\\期货 的目录\n",
      "\n",
      "2021/12/29  14:35    <DIR>          .\n",
      "2021/12/29  14:35    <DIR>          ..\n",
      "2021/12/24  10:40    <DIR>          .ipynb_checkpoints\n",
      "2021/11/24  11:45            32,757 Analy_RM.ipynb\n",
      "2021/11/24  11:36             4,336 DataGet_fromSina.ipynb\n",
      "2021/12/24  14:27            38,190 get_from_米匡.ipynb\n",
      "2021/11/29  15:49             7,147 getfrom tianqin.ipynb\n",
      "2021/11/15  18:34             4,377 GetFrom64.ipynb\n",
      "2021/11/16  17:30           377,118 RSI.ipynb\n",
      "2021/12/29  14:35            29,215 toshare_basic.ipynb\n",
      "2021/11/29  16:27            18,883 TqSDK.ipynb\n",
      "2021/12/20  11:32           348,169 tushare_eg2201.ipynb\n",
      "2021/11/25  14:38           394,398 tushare_Kline.ipynb\n",
      "2021/11/25  16:25           359,450 tushare_ma2201.ipynb\n",
      "2021/12/21  11:21           339,591 tushare_rb2201_rb2205.ipynb\n",
      "2021/11/09  14:12            11,376 环境安装.ipynb\n",
      "2021/12/20  11:14               858 通用分析方法.ipynb\n",
      "              14 个文件      1,965,865 字节\n",
      "               3 个目录 228,473,704,448 可用字节\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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